Scaling Graph Intelligence: Serverless GNNs for Real-Time Security

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Author: Denis Avetisyan

A new serverless architecture efficiently handles the demands of graph neural network-based intrusion detection, delivering low latency and high scalability.

GraphFaaS establishes a framework for function-as-a-service by constructing graphs, embedding nodes serverlessly, and performing serverless graph neural network inference, thereby enabling scalable and efficient execution of graph-based computations.
GraphFaaS establishes a framework for function-as-a-service by constructing graphs, embedding nodes serverlessly, and performing serverless graph neural network inference, thereby enabling scalable and efficient execution of graph-based computations.

GraphFaaS leverages graph filtering, adaptive partitioning, and dynamic resource scaling to achieve burst-resilient, real-time performance for GNN inference.

While graphical machine learning offers promising solutions for provenance-based intrusion detection, traditional statically-provisioned architectures struggle with both consistently low latency and the highly irregular workloads characteristic of real-time cybersecurity. This paper introduces GraphFaaS: Serverless GNN Inference for Burst-Resilient, Real-Time Intrusion Detection, a novel serverless architecture that dynamically scales GNN inference pipelines to address these challenges. By leveraging graph filtering, adaptive partitioning, and the elasticity of serverless computing, GraphFaaS demonstrably reduces detection latency and improves system stability. Could this approach unlock new possibilities for dependable, scalable, and responsive intrusion detection systems in increasingly complex cyber environments?

Provenance Graphs: The Foundation of Modern Security

Contemporary digital systems meticulously record the history of data and interactions as provenance graphs – complex networks detailing each step of a process, from creation to modification. However, the sheer scale of these graphs, generated by modern workloads, presents a significant challenge to traditional intrusion detection systems. These systems, often designed for simpler environments, struggle to efficiently traverse and analyze such vast datasets in real-time. The result is a bottleneck where potentially malicious activities can go unnoticed within the intricate web of interactions, hindering effective security monitoring and response. Consequently, new approaches are needed to harness the power of provenance data while overcoming the limitations of existing security infrastructure.

Conventional intrusion detection systems, such as those employing reactive signature-based approaches like Flash, frequently struggle to maintain security in modern computing environments. These systems are fundamentally limited by their dependence on known threat patterns; novel attacks or subtle deviations from established baselines often bypass detection. More critically, contemporary workloads are characterized by unpredictable bursts of activity and rapidly shifting operational parameters, creating a scalability bottleneck for systems designed around static analysis. The sheer volume of data generated by these dynamic environments overwhelms traditional IDSs, hindering their ability to process information in real-time and accurately identify malicious activity before significant damage occurs. This reactive posture and inherent scalability issue necessitate a shift towards proactive, adaptable security solutions capable of analyzing complex system interactions as they unfold.

The efficacy of modern security hinges on the detailed scrutiny of provenance graphs – comprehensive records of interactions within a system. These graphs aren’t merely historical logs; they represent a dynamic map of relationships, allowing analysts to trace the lineage of data and operations. Anomalous behaviors, often indicative of breaches, manifest as deviations from established patterns within these graphs. For instance, a process unexpectedly accessing sensitive data, or a user account exhibiting unusual activity, will leave a distinct trace. By employing graph analytics and machine learning algorithms, security systems can automatically identify these deviations, flagging potential threats before they escalate. The ability to pinpoint the origin and spread of malicious activity within the provenance graph is therefore paramount, transforming reactive security measures into proactive defenses capable of anticipating and neutralizing attacks in real-time.

Detection workload fluctuates significantly over time when processing the DARPA TC dataset.
Detection workload fluctuates significantly over time when processing the DARPA TC dataset.

GraphFaaS: A Serverless Architecture for Scalable Threat Detection

GraphFaaS implements an intrusion detection system by applying Graph Neural Networks (GNNs) directly to provenance graphs. Provenance graphs represent the lineage of data, detailing its origins and transformations, and are utilized as the input structure for GNN analysis. This approach allows the system to identify malicious activities by analyzing relationships and patterns within the data’s history, rather than relying solely on individual data points. The architecture is designed to process these graphs in a serverless manner, enabling scalable and efficient detection of intrusions based on the graph structure and node attributes.

GraphFaaS leverages the OpenFaaS serverless framework to provide dynamic scalability for intrusion detection. This architecture allows for automatic scaling of function instances – the computational units processing graph data – based on workload demands. Specifically, OpenFaaS manages the provisioning and execution of these instances, responding to increases in network traffic or the emergence of new attack patterns without manual intervention. This is critical for handling bursty workloads, common in network security, and for adapting to evolving threats that require rapid adjustments in processing capacity. The serverless design eliminates the need for pre-provisioned infrastructure, reducing operational costs and improving resource utilization by only allocating resources when actively processing data.

Graph partitioning is a core component of GraphFaaS’s scalability strategy. Large provenance graphs are divided into smaller, non-overlapping subgraphs, with each subgraph then processed by an independent OpenFaaS function instance. This decomposition allows for parallel computation, significantly reducing the overall processing time for extensive graphs. The partitioning process aims to minimize edge cuts between subgraphs to reduce communication overhead between function instances, while also balancing the computational load across those instances. Specifically, GraphFaaS employs a recursive bisectioning approach, iteratively dividing the graph until subgraph sizes are suitable for individual function execution, and leverages graph databases to manage and distribute these partitioned subgraphs efficiently.

Optimizing Graph Analysis with Precision Techniques

GraphFaaS leverages node embedding techniques to represent nodes within provenance graphs as numerical vectors. This transformation enables Graph Neural Networks (GNNs) to effectively process and learn from the graph data. Node embeddings capture the inherent characteristics and relationships of each node, converting categorical or textual attributes into a continuous vector space. The resulting vectors serve as input features for GNNs, allowing them to identify complex patterns and dependencies within the provenance graph that would be difficult to discern from raw attribute data. The dimensionality of these vectors is a configurable parameter, balancing information retention with computational efficiency.

GraphFaaS incorporates frequency-based filtering as a performance optimization technique by prioritizing edges and nodes based on their occurrence rates within the provenance graph. This filtering process reduces the computational load associated with graph traversal and analysis by focusing on the most frequently accessed elements. The system calculates the frequency of each edge and node, and a threshold is applied to retain only those exceeding a defined value; less frequent elements are excluded from subsequent processing. This targeted approach minimizes unnecessary computations while preserving critical relationships for accurate graph analysis, ultimately leading to faster query execution and improved scalability.

GraphFaaS utilizes the Best-Fit Algorithm to partition provenance graphs into subgraphs for parallel processing across multiple function instances. This algorithm aims to minimize data transfer and maximize workload balance by iteratively assigning nodes to instances based on their degree and connectivity. The partitioning process considers K-Hop Neighborhoods, analyzing relationships up to $K$ degrees of separation from a given node, to ensure that strongly connected components remain within a single function instance. This approach facilitates efficient distributed graph analysis by reducing communication overhead and improving overall scalability, as the analysis considers contextual relationships beyond direct adjacency.

Achieving Robust and Scalable Security Posture

Modern intrusion detection systems often struggle with the speed required to effectively counter rapidly evolving threats; however, GraphFaaS addresses this limitation with a substantial reduction in detection latency. Evaluations demonstrate that this system identifies malicious activity in an average of 2.10 seconds – a 6.7x improvement over the 14.16 seconds required by traditional methods. This accelerated detection isn’t simply about speed, but about enabling a more proactive security posture, allowing for faster containment and mitigation of attacks before significant damage occurs. The system’s ability to drastically shorten the window between intrusion and response represents a significant leap forward in real-time threat management.

GraphFaaS is engineered for adaptability through its serverless architecture and innovative graph partitioning techniques. This design allows the system to dynamically allocate resources as workload demands fluctuate, ensuring consistent performance even with exponentially increasing data volumes and network traffic. By dividing the complex security graph into smaller, manageable partitions, GraphFaaS distributes the processing load across multiple serverless functions, circumventing the bottlenecks often encountered in traditional, monolithic security systems. This partitioning not only enhances scalability but also improves resilience; if one partition experiences issues, the others continue functioning uninterrupted, maintaining a consistent level of threat detection. The result is a security solution that grows seamlessly with the infrastructure, accommodating future expansion without requiring costly hardware upgrades or complex reconfigurations.

GraphFaaS presents a compelling security solution by integrating the analytical strength of Graph Neural Networks (GNNs) with a serverless, scalable infrastructure. This combination not only facilitates efficient threat detection but also ensures consistent performance even under fluctuating loads. Demonstrably, GraphFaaS achieved a 64% reduction in the coefficient of variation (CV) of detection times—decreasing from $1.46$ to $0.52$. This improvement signifies a substantial increase in the stability and predictability of the system, meaning that response times are far less variable and more reliable than traditional methods, ultimately bolstering the security posture of modern, dynamic systems.

The pursuit of minimizing latency, as demonstrated by GraphFaaS, echoes a fundamental tenet of rigorous computation. Carl Friedrich Gauss famously stated, “I would rather explain my operations and calculations so that anyone could follow them.” This sentiment directly informs the design choices within the paper, particularly the emphasis on provenance graphs and adaptive partitioning. Just as Gauss prioritized clarity and verifiability in mathematical proofs, GraphFaaS prioritizes a transparent and scalable architecture to ensure predictable performance, even under burst traffic. The system’s ability to dynamically scale resources isn’t simply about speed; it’s about maintaining a provable level of correctness and reliability in real-time intrusion detection.

What’s Next?

The presented work, while demonstrating a functional architecture for serverless GNN inference, merely skirts the edges of true determinism. The claim of “burst resilience” hinges on dynamic scaling, a process inherently subject to external, and therefore unpredictable, factors. To speak of reliability without addressing the stochastic nature of cloud resource allocation feels… optimistic. A truly robust system demands provable latency bounds, not merely observed performance under specific load conditions.

Future work must confront the limitations of graph partitioning algorithms themselves. Current methods prioritize load balancing, but neglect the critical issue of data provenance. A compromised node, even if partitioned, can introduce subtle, yet catastrophic, errors. Establishing a verifiable chain of custody for graph data—a cryptographic guarantee of integrity—remains a significant, and largely unaddressed, challenge.

Ultimately, the pursuit of scalable intrusion detection necessitates a shift in perspective. The focus should not be solely on minimizing latency, but on maximizing confidence. A system that operates slowly, yet provides a mathematically verifiable guarantee of accuracy, is demonstrably superior to one that offers speed at the expense of certainty. The elegance of a solution, after all, lies not in its efficiency, but in its correctness.

Original article: https://arxiv.org/pdf/2511.10554.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-16 13:30